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PostgreSQL: the database that scales.

Database design, query optimization, and scaling strategies with PostgreSQL. The most advanced open-source database.

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How we deliver PostgreSQL projects

01

Schema design

We design normalized schemas with proper data types, constraints, and relationships, built for your query patterns and growth trajectory.

02

Optimization & indexing

We analyze query plans, add targeted indexes, rewrite slow queries, and configure connection pooling for maximum throughput.

03

Scaling & replication

We set up read replicas, connection pooling with PgBouncer, partitioning strategies, and automated backups for production reliability.

PostgreSQL

What we build

Schema Design

Normalized database schemas with proper constraints, data types, and relationships optimized for your application queries.

Query Optimization

EXPLAIN ANALYZE-driven query tuning, common table expression refactoring, and materialized views for complex reporting.

Index Strategy

B-tree, GIN, GiST, and BRIN indexes placed strategically based on actual query patterns and table statistics.

Replication & HA

Streaming replication, read replicas, and automatic failover with Patroni or AWS RDS Multi-AZ for high availability.

PGVector for AI

Vector similarity search with pgvector. HNSW and IVFFlat indexes for fast nearest-neighbor queries in your AI pipeline.

Backup & Recovery

Automated backups with point-in-time recovery, WAL archiving, and disaster recovery runbooks for production databases.

FAQ

FAQ

Yes. We audit your schema, analyze slow queries with EXPLAIN ANALYZE, review your indexing strategy, and implement changes that typically cut query times by 50-90% without application changes.
PostgreSQL is our default recommendation for most applications. It handles relational data, JSON, full-text search, and vector embeddings. We recommend specialized databases only when your workload specifically demands it.
We use zero-downtime migration strategies: adding columns as nullable, backfilling data in batches, and swapping constraints only after data is consistent. No maintenance windows required.
Yes. The pgvector extension adds vector similarity search directly in PostgreSQL. We set up vector columns, indexing with HNSW or IVFFlat, and integrate it with your AI pipeline. No separate vector database needed.

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